• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.17-24
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• 2008

Traffic accident forecasting model has been developed steadily to understand factors affecting traffic accidents and to reduce them. In Korea, the length of highways is over 3,000km, and it is within the top ten in the world. However, the number of accidents-per-one kilometer highway is higher than any other countries. The rapid increase of travel demand and transportation infrastructures since 1980's may influence on the high rates of traffic accident. Accident severity is one of the important indices as well as the rate of accident and factors such as road geometric conditions, driver characteristics and type of vehicles may be related to traffic accident severity. However, since all these factors are interacted complicatedly, the interactions are not easily identified. A structural equations model is adopted to capture the complex relationships among variables. In the model estimation, we use 2,880 accident data on highways in Korea. The SEM with several factors mentioned above as endogenous and exogenous variables shows that they have complex and strong relationships.

• Journal of Korean Society of Transportation
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• v.34 no.4
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• pp.354-372
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• 2016

This study identified factors affecting the crash severity at freeway work zones. A nice feature of this study was to take into account the characteristics of work zone traffic management in analyzing traffic safety concerns. In addition to crash records, vehicle detection systems (VDS) data and work zone historical data were used for establishing a dataset to be used for statistical analyses based on an ordered probit model. A total of six safety improvement strategies for freeway work zones, including traffic merging method, guidance information provision, speed management, warning information systems, traffic safety facility, and monitoring of effectiveness for countermeasures, were also proposed.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2016.11a
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• pp.259-260
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• 2016

어린이의 경우 다른 연령층에 비해 신체적, 정신적으로 완성되지 못하여 교통사고의 가능성이 높으며, 특히 전국의 어린이 교통사고는 점진적으로 감소 추세이나 인천의 어린이 교통사고는 감소하다가 다시 증가 추세에 들어선 실정이다. 따라서 본 연구의 목적은 어린이 교통사고 심각도에 영향을 미치는 주요 요인들을 발견하고 제시하고자 하였다. 순서형 로지스틱 회귀분석을 활용하여 순서척도인 반응변수에 대한 설명변수의 오즈(Odds)를 확인하고자 하였으며 안전운전불이행, 차대사람(횡단중), 차대차(측면직각충돌)사고가 유의한 결과로 나타났다. 안전운전불이행으로 인한 사망사고와 기타사고의 오즈차이는 1.35배, 측면직각충돌로 인한 사망사고와 기타사고의 오즈차이는 1.76배 증가하는 것으로 나타났고, 횡단중인 경우에는 오히려 사망 위험도의 오즈값이 0.58배로 감소하는 것으로 나타났다.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.8
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• pp.301-310
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• 2021

The TATI model is a Traffic Accident Text to RGB Image model, which is a methodology proposed in this paper for predicting the severity of traffic accidents. Traffic fatalities are decreasing every year, but they are among the low in the OECD members. Many studies have been conducted to reduce the death rate of traffic accidents, and among them, studies have been steadily conducted to reduce the incidence and mortality rate by predicting the severity of traffic accidents. In this regard, research has recently been active to predict the severity of traffic accidents by utilizing statistical models and deep learning models. In this paper, traffic accident dataset is converted to color images to predict the severity of traffic accidents, and this is done via CNN models. For performance comparison, we experiment that train the same data and compare the prediction results with the proposed model and other models. Through 10 experiments, we compare the accuracy and error range of four deep learning models. Experimental results show that the accuracy of the proposed model was the highest at 0.85, and the second lowest error range at 0.03 was shown to confirm the superiority of the performance.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.4
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• pp.1-12
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• 2017

Traffic accidents are caused by a combination of human factors, vehicle factors, and environmental factors. In the case of traffic accidents where rental cars are involved, the possibility and the severity of traffic accidents are expected to be different from those of other traffic accidents due to the unfamiliar environment of the driver. In this study, we developed a model to forecast the severity of rental car accidents by using Naive Bayes classifier for Busan, Gangneung, and Jeju city. In addition, we compared the prediction accuracy performance of two models where one model uses the variables of which statistical significance were verified in a prior study and another model uses the entire available variables. As a result of the comparison, it is shown that the prediction accuracy is higher when using the variables with statistical significance.

• Journal of Korean Society of Transportation
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• v.31 no.6
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• pp.53-66
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• 2013

The major purpose of this study is to evaluate methodologies to predict the injury severity of pedestrian-vehicle collisions. Methodologies to be evaluated and compared in this study include Binary Logistic Regression(BLR), Ordered Probit Model(OPM), Support Vector Machine(SVM) and Decision Tree(DT) method. Valuable insights into applying methodologies to analyze the characteristics of pedestrian injury severity are derived. For the purpose of identifying causal factors affecting the injury severity, statistical approaches such as BLR and OPM are recommended. On the other hand, to achieve better prediction performance, heuristic approaches such as SVM and DT are recommended. It is expected that the outcome of this study would be useful in developing various countermeasures for enhancing pedestrian safety.

• International Journal of Highway Engineering
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• v.11 no.2
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• pp.195-203
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• 2009

This research wished to risk type and examine closely driver special quality and relation of traffic accidents by occurrence type of traffic accidents and traffic accidents seriousness examine closely relation with Severity. Fractionate traffic accidents type by eight, and driver's special quality for risk group's classification did to distinction of sex, vehicle type, age etc. analyzed relation with injury degree adding belt used putting on availability for security the objectivity with wave. Used log-Linear model and Logit model for analysis of category data. A head-on collision and overtaking accident, right-turn accident are high injury or death accident and possibility to associate in relation with accident type and seriousness degree. In risk group analysis The age less than 20 years in motor-cycle driver, taxi driver in 41 years to 50 years old are very dangerous. The woman also was construed to the more risk group than man from when related to car, mini-bus, goods vehicle etc. Therefore, traffic safety education and Enforcement for risk group that way that can reduce accident that produce to reduce a loss of lives at traffic accidents appearance a head-on collision and overtaking accidents, right-turn accidents should be studied and as traffic accidents weakness class may have to be solidified.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1123-1130
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• 2020

This study aims to classify the severity in car crashes using five classification learning models. The dataset used in this study contains 21,013 vehicle crashes, obtained from Korea Expressway Corporation, between the year of 2015-2017 and the LightGBM(Light Gradient Boosting Model) performed well with the highest accuracy. LightGBM, the number of involved vehicles, type of accident, incident location, incident lane type, types of accidents, types of vehicles involved in accidents were shown as priority factors. Based on the results of this model, the establishment of a management strategy for response of highway traffic accident should be presented through a consistent prediction process of accident severity level. This study identifies applicability of Machine Learning Models for Predicting of the Severity of Car Traffic Accidents on a Highway and suggests that various machine learning techniques based on big data that can be used in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.3
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• pp.1105-1113
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• 2013

Understanding the characteristics of truck-involved crashes is of keen interest because such crashes are highly associated with greater potential leading to severer injury. The purpose of this study is to identify factors affecting injury severity of truck-involved crashes on freeways. In addition, a binary logistic regression technique is applied to identify causal factors affecting truck crash severity under normal and adverse weather conditions. Major findings from the analyses are discussed with truck operations strategies including speed enforcement, variable speed limit, and truck lane restriction, from the safety enhancement point of view. The results of this study would be useful for developing traffic control and operations strategies to reduce truck-involved crashes and injury severity in practice.

• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.59-67
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• 2004

On January 1, 2003, the motor vehicle management system in Korea was transformed from government's vehicle type approval to manufacturer's self-certification. The motor vehicle recall system with self-certification is an essential mechanism to place the liability of the vehicle defects on manufacturers and hence protect consumers from automobile accidents. This study provides a methodology to measure the benefits of motor vehicle recall system in two categories: benefits of reduction in traffic accidents and benefits of severity reduction in traffic accident. Applying the proposed methodology, the benefits of implementing motor vehicle recall system in Korea were estimated. It was estimated that 745 traffic accidents, 12 fatal accidents, and 1473 injury accidents were respectively reduced in 2002 due to implementation of motor vehicle recall system.